Hugging Face's logo

Datasets:
YiminJimmy
/
SARLANG-1M

ArXiv:
Dataset card Files
xet
Community
2
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    UnidentifiedImageError
Message:      cannot identify image file <_io.BytesIO object at 0x7f7eb5ff1b70>
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
                  batch = formatter.format_batch(pa_table)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/image.py", line 190, in decode_example
                  image = PIL.Image.open(bytes_)
                          ^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/PIL/Image.py", line 3498, in open
                  raise UnidentifiedImageError(msg)
              PIL.UnidentifiedImageError: cannot identify image file <_io.BytesIO object at 0x7f7eb5ff1b70>

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

SARLANG-1M

ArXiv Paper Github Project

SARLANG-1M is a large-scale benchmark tailored for multimodal SAR image understanding.
SARLANG-1M is a large-scale benchmark tailored for multimodal SAR image understanding, with a primary focus on integrating SAR with textual modality. SARLANG-1M comprises more than 1 million high-quality SAR image-text pairs collected from over 59 cities worldwide. It features hierarchical resolutions (ranging from 0.1 to 25 meters), fine-grained semantic descriptions (including both concise and detailed captions), diverse remote sensing categories (1,696 object types and 16 land cover classes), and multi-task question-answering pairs spanning seven applications and 1,012 question types.

SARLANG-1M dataset supports seven SAR applications:

Application Application Description Text Numuber
Image Description Describe the SAR image 45,650
Object Identification Determine the presence of specific objects 484,620
Object Classification Identify the predominant category within the SAR image 132,525
Instance Counting Quantify instances within the SAR image 117,382
Region Referring Determine the category present in the specific location 221,450
Object Positioning Determines the approximate location of a category 106,171
Others Predict the object shape, direction, reasoning etc 18,479

The Statistics of Text Annotations in SARLANG-1M dataset:

Statistics

The image shows the distribution of seven applications provided in the SARLANG-1M benchmark (a), the numbers of each question type in the 'others' application (b), and the distribution of the 30 most frequent object categories (c).

Some Representative SAR VQA Labels

There are 30 question-answering pairs and corresponding SAR images provided in the Examples.zip file. The entire SARLANG-1M dataset is coming soon!

Processed SAR Images in Our SARLANG-1M Dataset

The SAR images provided in our SARLANG-1M dataset come from four sub-datasets:

  • SpaceNet6, DFC2023, OpenEarthMap-SAR: The original SAR images(tif format) and the preprocessed SAR images(png format) are saved in the SARimages_original.zip file and the SARimages_preprocessed.zip file, respectively. Notably, SAR image preprocessing is an optional strategy to improve the performance of VLMs by significantly enhancing image clarity and effectively highlighting key objects within the SAR images. You can choose any version according to your needs.
  • SARDet-100K: The SAR images in this dataset has been preprocessed and denoised. Original SAR images are directly collected in our SARLANG-1M dataset without any preprocessing operations. The images can be directly download from the official link of SARDet_100K.

🤝Acknowledgments

The authors would also like to give special thanks to SARDet_100K, SpaceNet6, DFC2023 and OpenEarthMap-SAR for providing the valuable SAR Images.

Contact

2364356729@qq.com, YIMIN WEI, The University of Tokyo

Downloads last month
195

Paper for YiminJimmy/SARLANG-1M